No GPU automatically detected. Setting SETTINGS.GPU to 0, and SETTINGS.NJOBS to cpu_count.

Data Collection

This section of the notebook contains the functions that query the different datasets. This section is intentionally not included in this output.

Pull Data PIT:

Since data was queried directly from databases, clean samples of each were created and provided separately.

Pull TimeSeries of Data:

Overall Ratings Data Processing

	human_capital_theme_weight	human_capital_theme_score	human_capital_dev_weight	human_capital_dev_score	human_capital_dev_exp_score	human_capital_dev_mgmt_score
count	14235.000000	14235.000000	14235.000000	14235.000000	14235.000000	14235.000000
mean	19.730032	4.595265	10.684370	4.789055	6.700808	4.499747
std	7.752800	2.030963	10.750471	2.274230	1.732765	2.021378
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	15.000000	3.100000	0.000000	3.100000	5.700000	3.100000
50%	20.000000	4.600000	13.000000	4.800000	7.000000	4.500000
75%	25.000000	6.000000	20.000000	6.400000	8.100000	6.200000
max	56.000000	10.000000	35.000000	10.000000	10.000000	10.000000

Iterative Clustering of Data

HCM Data Processing

	workforce_part_time_over_20pct_nm	women_exec_mgmt_pct_recent	women_workforce_pct_recent	emp_turnover_annual_pct_recent	prof_dev_train_hours_per_emp_recent	women_senior_mgmt_pct_recent	human_capital_dev_med_risk_bus_pct	human_capital_dev_high_risk_bus_pct
count	9762.000000	7108.000000	4641.000000	2650.000000	2318.000000	1237.000000	10917.000000	10917.000000
mean	-0.035239	17.205867	34.834044	11.519623	30.897006	20.489491	46.010725	44.745759
std	0.184392	17.426977	18.473922	10.338212	37.585408	16.981514	46.716790	47.220603
min	-1.000000	0.000000	0.000000	0.000000	0.030000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	20.800000	5.000000	11.800000	6.300000	0.000000	0.000000
50%	0.000000	14.300000	34.000000	9.000000	21.000000	18.900000	24.310000	13.100000
75%	0.000000	27.300000	48.000000	15.000000	37.247500	33.300000	100.000000	100.000000
max	0.000000	100.000000	100.000000	133.000000	682.030000	100.000000	100.000000	100.000000

['women_exec_mgmt_pct_recent',
 'women_workforce_pct_recent',
 'emp_turnover_annual_pct_recent',
 'prof_dev_train_hours_per_emp_recent',
 'women_senior_mgmt_pct_recent',
 'human_capital_dev_high_risk_bus_pct']

	women_exec_mgmt_pct_recent	women_workforce_pct_recent	emp_turnover_annual_pct_recent	prof_dev_train_hours_per_emp_recent	women_senior_mgmt_pct_recent	human_capital_dev_high_risk_bus_pct
count	7108.000000	4641.000000	2650.000000	2318.000000	1237.000000	10917.000000
mean	17.205867	34.834044	11.519623	30.897006	20.489491	44.745759
std	17.426977	18.473922	10.338212	37.585408	16.981514	47.220603
min	0.000000	0.000000	0.000000	0.030000	0.000000	0.000000
25%	0.000000	20.800000	5.000000	11.800000	6.300000	0.000000
50%	14.300000	34.000000	9.000000	21.000000	18.900000	13.100000
75%	27.300000	48.000000	15.000000	37.247500	33.300000	100.000000
max	100.000000	100.000000	133.000000	682.030000	100.000000	100.000000

	women_exec_mgmt_pct_recent	women_workforce_pct_recent	emp_turnover_annual_pct_recent	prof_dev_train_hours_per_emp_recent	women_senior_mgmt_pct_recent	human_capital_dev_high_risk_bus_pct
count	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000
mean	17.457342	36.549789	10.915612	31.084979	23.013924	41.956667
std	18.066189	18.636729	9.481541	26.832484	15.846943	45.000002
min	0.000000	1.700000	0.000000	1.210000	0.000000	0.000000
25%	0.000000	21.500000	5.000000	13.000000	11.100000	0.000000
50%	14.600000	36.100000	9.000000	23.350000	23.100000	17.210000
75%	28.600000	52.000000	14.000000	40.000000	33.300000	99.990000
max	80.000000	78.500000	70.000000	173.000000	66.900000	100.000000

	workforce_part_time_over_20pct_nm	women_exec_mgmt_pct_recent	women_workforce_pct_recent	emp_turnover_annual_pct_recent	prof_dev_train_hours_per_emp_recent	women_senior_mgmt_pct_recent	human_capital_dev_med_risk_bus_pct	human_capital_dev_high_risk_bus_pct
count	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000
mean	-0.059072	17.457342	36.549789	10.915612	31.084979	23.013924	49.967046	41.956667
std	0.236258	18.066189	18.636729	9.481541	26.832484	15.846943	44.978101	45.000002
min	-1.000000	0.000000	1.700000	0.000000	1.210000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	21.500000	5.000000	13.000000	11.100000	0.000000	0.000000
50%	0.000000	14.600000	36.100000	9.000000	23.350000	23.100000	51.110000	17.210000
75%	0.000000	28.600000	52.000000	14.000000	40.000000	33.300000	100.000000	99.990000
max	0.000000	80.000000	78.500000	70.000000	173.000000	66.900000	100.000000	100.000000

Iterative Clustering of Data

Calculate Work Force Sophistication Score

	workforce_part_time_over_20pct_nm	women_exec_mgmt_pct_recent	women_workforce_pct_recent	emp_turnover_annual_pct_recent	prof_dev_train_hours_per_emp_recent	women_senior_mgmt_pct_recent	human_capital_dev_med_risk_bus_pct	human_capital_dev_high_risk_bus_pct	low	med	high	score
count	9762.000000	7108.000000	4641.000000	2650.000000	2318.000000	1237.000000	10917.000000	10917.000000	10917.000000	10917.000000	10917.000000	10917.000000
mean	-0.035239	17.205867	34.834044	11.519623	30.897006	20.489491	46.010725	44.745759	9.243517	46.010725	44.745759	67.751121
std	0.184392	17.426977	18.473922	10.338212	37.585408	16.981514	46.716790	47.220603	27.032061	46.716790	47.220603	30.571952
min	-1.000000	0.000000	0.000000	0.000000	0.030000	0.000000	0.000000	0.000000	-0.010000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	20.800000	5.000000	11.800000	6.300000	0.000000	0.000000	0.000000	0.000000	0.000000	50.000000
50%	0.000000	14.300000	34.000000	9.000000	21.000000	18.900000	24.310000	13.100000	0.000000	24.310000	13.100000	55.080000
75%	0.000000	27.300000	48.000000	15.000000	37.247500	33.300000	100.000000	100.000000	0.000000	100.000000	100.000000	100.000000
max	0.000000	100.000000	100.000000	133.000000	682.030000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000

invalid value encountered in greater_equal
invalid value encountered in less_equal

	workforce_part_time_over_20pct_nm	women_exec_mgmt_pct_recent	women_workforce_pct_recent	emp_turnover_annual_pct_recent	prof_dev_train_hours_per_emp_recent	women_senior_mgmt_pct_recent	human_capital_dev_med_risk_bus_pct	human_capital_dev_high_risk_bus_pct	low	med	high	score
count	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000
mean	-0.059072	17.457342	36.549789	10.915612	31.084979	23.013924	49.967046	41.956667	8.076287	49.967046	41.956667	66.940190
std	0.236258	18.066189	18.636729	9.481541	26.832484	15.846943	44.978101	45.000002	24.979332	44.978101	45.000002	28.613392
min	-1.000000	0.000000	1.700000	0.000000	1.210000	0.000000	0.000000	0.000000	-0.010000	0.000000	0.000000	0.000000
25%	0.000000	0.000000	21.500000	5.000000	13.000000	11.100000	0.000000	0.000000	0.000000	0.000000	0.000000	50.000000
50%	0.000000	14.600000	36.100000	9.000000	23.350000	23.100000	51.110000	17.210000	0.000000	51.110000	17.210000	58.605000
75%	0.000000	28.600000	52.000000	14.000000	40.000000	33.300000	100.000000	99.990000	0.010000	100.000000	99.990000	99.990000
max	0.000000	80.000000	78.500000	70.000000	173.000000	66.900000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000

A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

Cluster_No_WF	0	1	2
women_exec_mgmt_pct_recent	9.121296	25.348962	20.230435
women_workforce_pct_recent	21.203704	50.070755	46.295652
emp_turnover_annual_pct_recent	9.111111	12.518868	12.000000
prof_dev_train_hours_per_emp_recent	22.691019	25.649340	95.551304
women_senior_mgmt_pct_recent	13.371296	31.395283	29.665217
score	65.654028	68.320660	66.617391

Cluster	0	1	2	3	4	5
women_exec_mgmt_pct_recent	6.849254	23.033333	11.555556	14.377500	34.359259	41.946154
women_workforce_pct_recent	17.562687	45.994444	35.493651	57.016667	43.929630	45.496154
emp_turnover_annual_pct_recent	9.776119	12.222222	8.301587	12.027778	15.592593	12.884615
prof_dev_train_hours_per_emp_recent	20.613284	103.792778	26.686825	38.863611	15.166296	24.151154
women_senior_mgmt_pct_recent	10.283582	29.444444	23.760317	27.133333	35.033333	31.373077
score	51.053284	67.566111	96.370000	52.551528	28.246667	96.240000

Clustering with Workforce Sophistication Score:

Percent of Variance Explained:
PCA Dimension 1:  33.14%
PCA Dimension 2:  30.12%
PCA Dimension 3:  17.96%
PCA Dimension 4:  10.02%
PCA Dimension 5:  5.66%
PCA Dimension 6:  3.1%

Total % of Variance Explained: 100.0%

0_Component Loadings:
women_exec_mgmt_pct_recent:  0.1433
women_workforce_pct_recent:  0.1957
emp_turnover_annual_pct_recent:  -0.0363
prof_dev_train_hours_per_emp_recent:  0.5078
women_senior_mgmt_pct_recent:  0.1201
score:  0.817
 
1_Component Loadings:
women_exec_mgmt_pct_recent:  0.1901
women_workforce_pct_recent:  0.2728
emp_turnover_annual_pct_recent:  0.0389
prof_dev_train_hours_per_emp_recent:  0.7249
women_senior_mgmt_pct_recent:  0.1811
score:  -0.5742
 
2_Component Loadings:
women_exec_mgmt_pct_recent:  0.4883
women_workforce_pct_recent:  0.5441
emp_turnover_annual_pct_recent:  0.1166
prof_dev_train_hours_per_emp_recent:  -0.4596
women_senior_mgmt_pct_recent:  0.4907
score:  0.0027
 

Clustering with NO Workforce Sophistication Score:

Percent of Variance Explained:
PCA Dimension 1:  45.81%
PCA Dimension 2:  26.43%
PCA Dimension 3:  14.75%

Total % of Variance Explained: 87.0%

0_Component Loadings:
women_exec_mgmt_pct_recent:  0.2384
women_workforce_pct_recent:  0.3358
emp_turnover_annual_pct_recent:  0.0083
prof_dev_train_hours_per_emp_recent:  0.8851
women_senior_mgmt_pct_recent:  0.2168
 
1_Component Loadings:
women_exec_mgmt_pct_recent:  0.4883
women_workforce_pct_recent:  0.5442
emp_turnover_annual_pct_recent:  0.1169
prof_dev_train_hours_per_emp_recent:  -0.4593
women_senior_mgmt_pct_recent:  0.4908
 
2_Component Loadings:
women_exec_mgmt_pct_recent:  0.794
women_workforce_pct_recent:  -0.5846
emp_turnover_annual_pct_recent:  -0.1433
prof_dev_train_hours_per_emp_recent:  0.0289
women_senior_mgmt_pct_recent:  -0.0804
 

	workforce_part_time_over_20pct_nm	women_exec_mgmt_pct_recent	women_workforce_pct_recent	emp_turnover_annual_pct_recent	prof_dev_train_hours_per_emp_recent	women_senior_mgmt_pct_recent	human_capital_dev_med_risk_bus_pct	human_capital_dev_high_risk_bus_pct	low	med	high	score	Cluster_No_WF	Cluster	human_capital_theme_weight	human_capital_theme_score	human_capital_dev_weight	human_capital_dev_score	human_capital_dev_exp_score	human_capital_dev_mgmt_score
count	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	225.000000	225.000000	225.000000	225.000000	225.000000	225.000000
mean	-0.059072	17.457342	36.549789	10.915612	31.084979	23.013924	49.967046	41.956667	8.076287	49.967046	41.956667	66.940190	0.641350	2.067511	20.102222	5.235111	9.373333	5.672444	6.735111	5.451556
std	0.236258	18.066189	18.636729	9.481541	26.832484	15.846943	44.978101	45.000002	24.979332	44.978101	45.000002	28.613392	0.652618	1.676002	8.419431	2.043099	10.379977	2.123812	1.795198	1.881633
min	-1.000000	0.000000	1.700000	0.000000	1.210000	0.000000	0.000000	0.000000	-0.010000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.500000	0.000000	0.000000	2.300000	0.700000
25%	0.000000	0.000000	21.500000	5.000000	13.000000	11.100000	0.000000	0.000000	0.000000	0.000000	0.000000	50.000000	0.000000	0.000000	15.000000	3.800000	0.000000	4.300000	5.900000	4.200000
50%	0.000000	14.600000	36.100000	9.000000	23.350000	23.100000	51.110000	17.210000	0.000000	51.110000	17.210000	58.605000	1.000000	2.000000	20.000000	5.200000	0.000000	5.700000	7.100000	5.600000
75%	0.000000	28.600000	52.000000	14.000000	40.000000	33.300000	100.000000	99.990000	0.010000	100.000000	99.990000	99.990000	1.000000	3.000000	25.000000	6.600000	17.000000	6.900000	8.000000	7.000000
max	0.000000	80.000000	78.500000	70.000000	173.000000	66.900000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	2.000000	5.000000	56.000000	10.000000	35.000000	10.000000	10.000000	9.300000

Cluster	0	1	2	3	4	5
workforce_part_time_over_20pct_nm	-0.059701	0.000000	-0.031746	-0.111111	-0.148148	0.000000
women_exec_mgmt_pct_recent	6.849254	23.033333	11.555556	14.377500	34.359259	41.946154
women_workforce_pct_recent	17.562687	45.994444	35.493651	57.016667	43.929630	45.496154
emp_turnover_annual_pct_recent	9.776119	12.222222	8.301587	12.027778	15.592593	12.884615
prof_dev_train_hours_per_emp_recent	20.613284	103.792778	26.686825	38.863611	15.166296	24.151154
women_senior_mgmt_pct_recent	10.283582	29.444444	23.760317	27.133333	35.033333	31.373077
human_capital_dev_med_risk_bus_pct	90.021791	31.810000	6.345079	86.797500	56.229630	7.519231
human_capital_dev_high_risk_bus_pct	6.042388	51.661111	93.197460	9.152778	0.131852	92.480385
low	3.935821	16.528889	0.457460	4.049722	43.638519	0.000385
med	90.021791	31.810000	6.345079	86.797500	56.229630	7.519231
high	6.042388	51.661111	93.197460	9.152778	0.131852	92.480385
score	51.053284	67.566111	96.370000	52.551528	28.246667	96.240000
Cluster_No_WF	0.029851	2.000000	0.396825	1.083333	0.851852	1.038462
human_capital_theme_weight	22.301587	21.812500	19.885246	16.138889	18.083333	21.640000
human_capital_theme_score	5.411111	4.543750	5.101639	5.997222	5.320833	4.380000
human_capital_dev_weight	2.380952	12.500000	11.786885	12.555556	3.875000	19.800000
human_capital_dev_score	6.003175	5.631250	4.885246	6.194444	7.337500	4.436000
human_capital_dev_exp_score	5.712698	6.793750	8.080328	6.638889	4.450000	8.324000
human_capital_dev_mgmt_score	4.796825	5.425000	5.963934	5.838889	4.983333	5.760000

Cluster_No_WF	0	1	2
workforce_part_time_over_20pct_nm	-0.064815	-0.066038	0.000000
women_exec_mgmt_pct_recent	9.121296	25.348962	20.230435
women_workforce_pct_recent	21.203704	50.070755	46.295652
emp_turnover_annual_pct_recent	9.111111	12.518868	12.000000
prof_dev_train_hours_per_emp_recent	22.691019	25.649340	95.551304
women_senior_mgmt_pct_recent	13.371296	31.395283	29.665217
human_capital_dev_med_risk_bus_pct	58.365278	44.923962	33.773913
human_capital_dev_high_risk_bus_pct	36.471389	45.858679	49.730435
low	5.163333	9.217358	16.495652
med	58.365278	44.923962	33.773913
high	36.471389	45.858679	49.730435
score	65.654028	68.320660	66.617391
Cluster	0.870370	3.415094	1.478261
human_capital_theme_weight	21.346535	18.291262	23.000000
human_capital_theme_score	5.254455	5.327184	4.690476
human_capital_dev_weight	5.920792	12.291262	11.666667
human_capital_dev_score	5.642574	5.711650	5.623810
human_capital_dev_exp_score	6.523762	6.958252	6.657143
human_capital_dev_mgmt_score	5.216832	5.716505	5.280952

invalid value encountered in greater_equal
invalid value encountered in less_equal

invalid value encountered in log

	ML Score	ML Rating PctTile	Z Score	Z Score Centered	Z Score Norm	human_capital_theme_weight	human_capital_theme_score	human_capital_dev_weight	human_capital_dev_score	human_capital_dev_exp_score	human_capital_dev_mgmt_score
count	327.000000	327.000000	3.270000e+02	327.000000	327.000000	301.000000	301.000000	301.000000	301.000000	301.000000	301.000000
mean	43.175941	5.015291	3.228814e-16	4.996594	5.021055	20.169435	5.137209	9.073090	5.597342	6.634884	5.265116
std	17.761754	2.891162	1.001533e+00	0.982389	2.007588	8.546024	1.984879	10.368284	2.061971	1.758222	1.823096
min	1.361333	0.030581	-2.357802e+00	2.642198	0.000000	0.000000	0.300000	0.000000	0.000000	2.300000	0.700000
25%	30.121333	2.522936	-7.361106e-01	4.263889	3.459567	15.000000	3.800000	0.000000	4.300000	5.700000	4.100000
50%	41.909333	5.015291	-7.142024e-02	4.928580	4.913046	20.000000	5.100000	0.000000	5.500000	6.600000	5.300000
75%	54.615333	7.507645	6.450334e-01	5.645033	6.479717	25.000000	6.400000	15.000000	6.800000	7.800000	6.700000
max	151.600000	10.000000	6.113711e+00	10.000000	10.000000	56.000000	10.000000	35.000000	10.000000	10.000000	9.300000

	ML Score	ML Rating PctTile	Z Score	Z Score Centered	Z Score Norm	human_capital_theme_weight	human_capital_theme_score	human_capital_dev_weight	human_capital_dev_score	human_capital_dev_exp_score	human_capital_dev_mgmt_score
count	301.000000	301.000000	301.000000	301.000000	301.000000	301.000000	301.000000	301.000000	301.000000	301.000000	301.000000
mean	43.574230	5.068325	0.022458	5.018758	5.065716	20.169435	5.137209	9.073090	5.597342	6.634884	5.265116
std	17.496269	2.839752	0.986563	0.965505	1.956938	8.546024	1.984879	10.368284	2.061971	1.758222	1.823096
min	6.073333	0.061162	-2.092106	2.907894	0.494409	0.000000	0.300000	0.000000	0.000000	2.300000	0.700000
25%	30.732000	2.629969	-0.701677	4.298323	3.534863	15.000000	3.800000	0.000000	4.300000	5.700000	4.100000
50%	41.977333	5.045872	-0.067586	4.932414	4.921431	20.000000	5.100000	0.000000	5.500000	6.600000	5.300000
75%	54.493333	7.492355	0.638154	5.638154	6.464674	25.000000	6.400000	15.000000	6.800000	7.800000	6.700000
max	151.600000	10.000000	6.113711	10.000000	10.000000	56.000000	10.000000	35.000000	10.000000	10.000000	9.300000

Interaction with Policy Data

Possible Values for Degree Program Policy:
""
"Programs covering all employees (including part-time and contractors)"
"No evidence"
"Programs covering all permanent employees (excluding part-time and contractors)"
"General statements on training and development"

Encoding of Possible Values for Degree Program Policy: 

: 	 0
Programs covering all employees (including part-time and contractors): 	 1
No evidence: 	 -1
General statements on training and development: 	 0
Programs covering all permanent employees (excluding part-time and contractors): 	 1

Possible Values for Degree Program Policy:
""
"General statements on leadership training with unknown scope or achieved results"
"Comprehensive succession planning  & development programs at multiple levels"
"No evidence"
"Programs focusing on internal upward mobility through training and development"
"Minimum practices expected based on domestic industry norms"

Encoding of Possible Values for Degree Program Policy: 

: 	 0
General statements on leadership training with unknown scope or achieved results: 	 0
Comprehensive succession planning  & development programs at multiple levels: 	 1
No evidence: 	 -1
Minimum practices expected based on domestic industry norms: 	 0
Programs focusing on internal upward mobility through training and development: 	 1

	Degree Program	Dev Program	Overall Policy	workforce_part_time_over_20pct_nm	women_exec_mgmt_pct_recent	women_workforce_pct_recent	emp_turnover_annual_pct_recent	prof_dev_train_hours_per_emp_recent	women_senior_mgmt_pct_recent	human_capital_dev_med_risk_bus_pct	...	score	ML Score	ML Score with Policy	ML Score NO Policy	human_capital_theme_weight	human_capital_theme_score	human_capital_dev_weight	human_capital_dev_score	human_capital_dev_exp_score	human_capital_dev_mgmt_score
count	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	237.000000	...	237.000000	237.000000	237.000000	237.000000	225.000000	225.000000	225.000000	225.000000	225.000000	225.000000
mean	0.059072	0.645570	0.352321	-0.059072	17.457342	36.549789	10.915612	31.084979	23.013924	49.967046	...	66.940190	44.344805	27.657826	44.344805	20.102222	5.235111	9.373333	5.672444	6.735111	5.451556
std	0.593656	0.553218	0.426014	0.236258	18.066189	18.636729	9.481541	26.832484	15.846943	44.978101	...	28.613392	16.654415	18.026667	16.654415	8.419431	2.043099	10.379977	2.123812	1.795198	1.881633
min	-1.000000	-1.000000	-1.000000	-1.000000	0.000000	1.700000	0.000000	1.210000	0.000000	0.000000	...	0.000000	6.153333	-10.286667	6.153333	0.000000	0.500000	0.000000	0.000000	2.300000	0.700000
25%	0.000000	0.000000	0.000000	0.000000	0.000000	21.500000	5.000000	13.000000	11.100000	0.000000	...	50.000000	30.822000	15.680000	30.822000	15.000000	3.800000	0.000000	4.300000	5.900000	4.200000
50%	0.000000	1.000000	0.500000	0.000000	14.600000	36.100000	9.000000	23.350000	23.100000	51.110000	...	58.605000	44.765333	26.671333	44.765333	20.000000	5.200000	0.000000	5.700000	7.100000	5.600000
75%	0.000000	1.000000	0.500000	0.000000	28.600000	52.000000	14.000000	40.000000	33.300000	100.000000	...	99.990000	55.020000	38.246667	55.020000	25.000000	6.600000	17.000000	6.900000	8.000000	7.000000
max	1.000000	1.000000	1.000000	0.000000	80.000000	78.500000	70.000000	173.000000	66.900000	100.000000	...	100.000000	117.206000	98.113000	117.206000	56.000000	10.000000	35.000000	10.000000	10.000000	9.300000

8 rows × 24 columns

	count	mean	std	min	25%	50%	75%	max
Z Score Norm with Policy	70.0	5.65	2.62	0.61	3.68	5.36	8.03	10.0
Z Score Norm NO Policy	70.0	5.45	2.40	0.18	3.62	5.70	7.02	10.0
human_capital_dev_exp_score	70.0	6.91	2.11	2.30	5.93	6.95	8.50	10.0
human_capital_dev_score	70.0	5.75	2.54	0.70	4.05	5.65	7.48	10.0
human_capital_theme_score	70.0	5.53	2.77	0.80	3.42	5.80	7.80	10.0
human_capital_theme_weight	70.0	21.19	6.56	0.00	17.00	23.00	25.00	35.0

	count	mean	std	min	25%	50%	75%	max
Z Score Norm with Policy	167.0	4.79	1.68	0.61	3.64	4.71	5.88	9.25
Z Score Norm NO Policy	167.0	4.87	1.84	0.88	3.33	4.87	6.28	9.76
human_capital_dev_exp_score	155.0	6.66	1.63	2.30	5.90	7.10	7.80	10.00
human_capital_dev_score	155.0	5.64	1.91	0.00	4.40	5.70	6.80	10.00
human_capital_theme_score	155.0	5.10	1.60	0.50	3.85	5.10	6.30	9.60
human_capital_theme_weight	155.0	19.61	9.11	0.00	15.00	18.00	24.00	56.00

End of Python Jupyter Notebook